All the information and analyzes presented below were based on the work and information of the Murder Accountability Project, a nonprofit group organized in 2015 and dedicated to educate Americans on the importance of accurately accounting for unsolved homicides within the United States. They seek to obtain information from federal, state and local governments about unsolved homicides and to publish this information. The Project’s Board of Directors is composed of retired law enforcement investigators, investigative journalists, criminologists and other experts on various aspects of homicide.
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Note: This content was created in context of the course Introduction to R for Journalists: How to Find Great Stories in Data, created by the Knight Center and taught by Andrew Ba Tran.
explores among the more than 752 thousand records of murders grouped by state
Let’s explore how many murders per capita has each state in the country. Note: For this comparison, we use the Census Data API information, which can be consulted in United Estates Census Bureau
I try to find to Richard Ramirez an American serial killer Dubbed the ’Night Stalker
[Richard Ramirez] (https://www.biography.com/people/richard-ramirez-12385163)
photo:Night Stalker
Dubbed the ‘Night Stalker,’ Richard Ramirez was an American serial killer who killed at least 14 people and tortured dozens more before being captured in 1985.
My criteria for filtering was:
killed at least 14 people and tortured dozens more mostly during the spring and summer of 1985. Location was LA and San Francisco Pattern for the killer: The husband was shot first, then the wife was brutally assaulted and stabbed to death. first known murder on June 28, 1984 - the victim was 79-year-old Jennie Vincow, who was sexually assaulted, stabbed and killed. LA - San Francisco Cases was solved
Results: I found 14 cases that adapt to the characteristics of the killer.
library(readr)
county.fips <- read_csv("data/fips_counties.csv")
# FIPS change over time. Data tends to do that when you've got decades of stuff
# We'll swap out some County Names (most are from Alaska) before we join the data sets
murders2 <- murders %>%
mutate(CNTYFIPS=as.numeric(as.character(CNTYFIPS))) %>%
mutate(CNTYFIPS=case_when(
CNTYFIPS==51560 ~ 51005,
CNTYFIPS==2232 ~ 2105,
CNTYFIPS==2280 ~ 2195,
CNTYFIPS==2201 ~ 2198,
TRUE ~ CNTYFIPS
)) %>%
left_join(county.fips, by=c("CNTYFIPS"="fips"))
n_stalker1 <- murders2 %>%
filter(State=="California" & name_of_county=="Los Angeles"|
name_of_county=="San Francisco") %>%
filter(Year==1985) %>%
filter(Month_value>=3 & Month_value<=8) %>%
filter(Solved_label=="Yes") %>%
filter(OffSex_label=="Male") %>%
filter(OffAge==25) %>%
filter(Weapon_label=="Handgun - pistol, revolver, etc"|
Weapon_label=="Firearm, type not stated") %>%
filter(Homicide_label=="Murder and non-negligent manslaughter")
datatable(n_stalker1)